Not all crashes are created equal: Associations between the built environment and disparities in bicycle collisions
AbstractHistorically disadvantaged populations are disproportionately represented in bicycle crashes. Previous research has found that Black and Hispanic bicyclists and areas with higher populations of non-White residents, lower median income, and high poverty experience bicycle crashes more frequently than others. Although existing research has explored the role of socioeconomic status and the built environment in predicting crash frequency, few scholars have studied how these factors account for disparities along racial and ethnic lines. Using a database of 7,088 bicycle crashes over a three-year period in the San Francisco Bay Area, this study examines the influence of socioeconomic, transportation, and land-use characteristics as potential causes of differences in bicycle crash occurrences among racial and ethnic groups in the San Francisco Bay Area. While areas of high poverty and high land-use intensity are associated with higher numbers of bicycle crashes overall, lower-traffic streets and bicycle infrastructure do not affect the frequency of crashes involving Black and Hispanic cyclists. Black bicyclists have a disproportionate risk of being involved in a crash in poor urban neighborhoods, controlling for other factors. These findings draw attention to the need for planners to consider how socioeconomic differences and vulnerability at the neighborhood level play a role in safety.
Buehler, R., & Pucher, J. (2012). Cycling to work in 90 large American cities: New evidence on the role of bike paths and lanes. Transportation, 39(2), 409–432. https://doi.org/10.1007/s11116-011-9355-8
Cervero, R., & Duncan, M. (2003). Walking, bicycling, and urban landscapes: Evidence from the San Francisco Bay Area. American Journal of Public Health, 93(9), 1478–1483.
Chakravarthy, B., Anderson, C. L., Ludlow, J., Lotfipour, S., & Vaca, F. E. (2012). A geographic analysis of collisions involving child pedestrians in a large southern California county. Traffic Injury Prevention, 13(2), 193–198. https://doi.org/10.1080/15389588.2011.642034
Chen, C., Lin, H., & Loo, B. P. (2012). Exploring the impacts of safety culture on immigrants’ vulnerability in non-motorized crashes: A cross-sectional study. Journal of Urban Health, 89(1), 138–152. https://doi.org/10.1007/s11524-011-9629-7
Chen, P. (2015). Built environment factors in explaining the automobile-involved bicycle crash frequencies: A spatial statistic approach. Safety Science, 79, 336–343. https://doi.org/10.1016/j.ssci.2015.06.016
City and County of San Francisco. (2015). Vision Zero San Francisco Two-Year Action Strategy. San Francisco: City and County of San Francisco.
City of Chicago. (2017). Vision Zero Chicago Action Plan, 2017-2019. Chicago: City of Chicago.
City of Los Angeles. (2015). Vision Zero Los Angeles: 2015-2025. Los Angeles: City of Los Angeles.
City of Philadelphia. (2017). Vision Zero Three-Year Action Plan. Philadelphia: City of Philadelphia.
Cradock, A. L., Troped, P. J., Fields, B., Melly, S. J., Simms, S. V., Gimmler, F., & Fowler, M. (2009). Factors associated with federal transportation funding for local pedestrian and bicycle programming and facilities. Journal of Public Health Policy, 30(S1), S38–S72. https://doi.org/10.1057/jphp.2008.60
Delmelle, E. C., Thill, J.-C., & Ha, H.-H. (2012). Spatial epidemiologic analysis of relative collision risk factors among urban bicyclists and pedestrians. Transportation, 39(2), 433–448. https://doi.org/10.1007/s11116-011-9363-8
Dill, J., & Carr, T. (2003). Bicycle commuting and facilities in major U.S. cities: If you build them, commuters will use them. Transportation Research Record: Journal of the Transportation Research Board, 1828, 116–123. https://doi.org/10.3141/1828-14
Dill, J., & Voros, K. (2007). Factors affecting bicycling demand: Initial survey findings from the Portland, Oregon region. Transportation Research Record: Journal of the Transportation Research Board, 2031, 9–17. https://doi.org/10.3141/2031-02
Dupont, E., Papadimitriou, E., Martensen, H., & Yannis, G. (2013). Multilevel analysis in road safety research. Accident Analysis & Prevention, 60(Supplement C), 402–411. https://doi.org/10.1016/j.aap.2013.04.035
Epperson, B. (1995). Demographic and economic characteristics of bicyclists involved in bicycle-motor vehicle accidents. Transportation Research Record: Journal of the Transportation Research Board, 1502, 58–64.
Federal Highway Administration. (2009). National Household Travel Survey. Retrieved from http://nhts.ornl.gov/
Gladhill, K., & Monsere, C. (2012). Exploring traffic safety and urban form in Portland, Oregon. Transportation Research Record: Journal of the Transportation Research Board, 2318, 63–74. https://doi.org/10.3141/2318-08
Golub, A., Hoffman, M. L., Lugo, A. E., & Sandoval, G. F. (Eds.). (2016). Bicycle justice and urban transformation: Biking for all? New York, NY: Routledge.
Hamann, C., & Peek-Asa, C. (2013). On-road bicycle facilities and bicycle crashes in Iowa, 2007–2010. Accident Analysis & Prevention, 56, 103–109. https://doi.org/10.1016/j.aap.2012.12.031
Heinen, E., van Wee, B., & Maat, K. (2010). Commuting by bicycle: An overview of the literature. Transport Reviews: A Transnational Transdisciplinary Journal, 30(1), 59–96. https://doi.org/10.1080/01441640903187001
Huang, H., & Abdel-Aty, M. (2010). Multilevel data and Bayesian analysis in traffic safety. Accident Analysis & Prevention, 42(6), 1556–1565. https://doi.org/10.1016/j.aap.2010.03.013
Jacobsen, P. L., & Rutter, H. (2012). Cycling safety. In J. Pucher & R. Buehler (Eds.), City cycling (pp. 141–156). Cambridge, MA: The MIT Press.
Juhra, C., Wieskötter, B., Chu, K., Trost, L., Weiss, U., Messerschmidt, M., … Raschke, M. (2012). Bicycle accidents — do we only see the tip of the iceberg? Injury, 43(12), 2026–2034. https://doi.org/10.1016/j.injury.2011.10.016
Kim, D., & Kim, K. (2015). The influence of bicycle oriented facilities on bicycle crashes within crash concentrated areas. Traffic Injury Prevention, 16(1), 70–75. https://doi.org/10.1080/15389588.2014.895924
Kim, K., Pant, P., & Yamashita, E. (2010). Accidents and accessibility. Transportation Research Record: Journal of the Transportation Research Board, 2147, 9–17. https://doi.org/10.3141/2147-02
Knoblauch, R. L., Seifert, R. F., & Murphy, N. B. (2004). The pedestrian and bicyclist highway safety problem as it relates to the Hispanic population in the United States. Washington, DC: Federal Highway Administration. Retrieved from http://safety.fhwa.dot.gov/ped_bike/hispanic/03p00324/050329.pdf
Lee, J., Abdel-Aty, M., & Jiang, X. (2015). Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. Accident Analysis & Prevention, 78, 146–154. https://doi.org/10.1016/j.aap.2015.03.003
Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. https://doi.org/10.1016/j.tra.2010.02.001
Lubitow, A., & Miller, T. R. (2013). Contesting sustainability: Bikes, race, and politics in Portlandia. Environmental Justice, 6(4), 121–126. https://doi.org/10.1089/env.2013.0018
McArthur, A., Savolainen, P., & Gates, T. (2014). Spatial analysis of child pedestrian and bicycle crashes. Transportation Research Record: Journal of the Transportation Research Board, 2465, 57–63. https://doi.org/10.3141/2465-08
Minikel, E. (2012). Cyclist safety on bicycle boulevards and parallel arterial routes in Berkeley, California. Accident Analysis & Prevention, 45, 241–247. https://doi.org/10.1016/j.aap.2011.07.009
National Highway Traffic Safety Administration. (2016). Fatality analysis reporting system (FARS). Retrieved from http://www.nhtsa.gov/FARS
Noland, R. B., Klein, N. J., & Tulach, N. K. (2013). Do lower income areas have more pedestrian casualties? Accident Analysis & Prevention, 59, 337–345. https://doi.org/10.1016/j.aap.2013.06.009
Noland, R., & Quddus, M. (2004). Analysis of pedestrian and bicycle casualties with regional panel data. Transportation Research Record: Journal of the Transportation Research Board, 1897, 28–33. https://doi.org/10.3141/1897-04
Prelog, R. (2015). Equity of access to bicycle infrastructure. Washington, DC: League of American Bicyclists.
Pucher, J., & Buehler, R. (2006). Why Canadians cycle more than Americans: A comparative analysis of bicycling trends and policies. Transport Policy, 13(3), 265–279. https://doi.org/10.1016/j.tranpol.2005.11.001
Pucher, J., Dill, J., & Handy, S. (2010). Infrastructure, programs, and policies to increase bicycling: An international review. Preventive Medicine, 50(Supplement 1), S106–S125. https://doi.org/10.1016/j.ypmed.2009.07.028
Quddus, M. A. (2008). Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data. Accident Analysis & Prevention, 40(4), 1486–1497. https://doi.org/10.1016/j.aap.2008.03.009
Reynolds, C. C., Harris, M. A., Teschke, K., Cripton, P. A., & Winters, M. (2009). The impact of transportation infrastructure on bicycling injuries and crashes: A review of the literature. Environmental Health, 8, 47. https://doi.org/10.1186/1476-069X-8-47
Rodgers, G. B. (1997). Factors associated with the crash risk of adult bicyclists. Journal of Safety Research, 28(4), 233–241. https://doi.org/10.1016/S0022-4375(97)00009-1
Safe Transportation Research and Education Center. (2016). Transportation injury mapping system. Retrieved from http://tims.berkeley.edu/
Salon, D., & Handy, S. (2014). Estimating total miles walked and biked by census tract in California. Davis, CA: Institute of Transportation Studies, UC Davis.
Sanders, R. L. (2015). Perceived traffic risk for cyclists: The impact of near miss and collision experiences. Accident Analysis & Prevention, 75, 26–34. https://doi.org/10.1016/j.aap.2014.11.004
Sciortino, S., Vassar, M., Radetsky, M., & Knudson, M. M. (2005). San Francisco pedestrian injury surveillance: Mapping, under-reporting, and injury severity in police and hospital records. Accident Analysis & Prevention, 37(6), 1102–1113. https://doi.org/10.1016/j.aap.2005.06.010
Siddiqui, C., Abdel-Aty, M., & Choi, K. (2012). Macroscopic spatial analysis of pedestrian and bicycle crashes. Accident Analysis & Prevention, 45, 382–391. https://doi.org/10.1016/j.aap.2011.08.003
Stan Development Team. (2016). RStan: The R Interface to Stan (Version 2.12.1). Retrieved from http://mc-stan.org
Stein, S. (2011). Bike lanes and gentrification: New York City’s shades of green. Progressive Planning, 188(Winter), 34–37.
Stutts, J. C., Williamson, J. E., Whitley, T., & Sheldon, F. C. (1990). Bicycle accidents and injuries: A pilot study comparing hospital- and police-reported data. Accident Analysis & Prevention, 22(1), 67–78. https://doi.org/10.1016/0001-4575(90)90008-9
Teschke, K., Harris, M. A., Reynolds, C. C., Winters, M., Babul, S., Chipman, M., … Cripton, P. A. (2012). Route infrastructure and the risk of injuries to bicyclists: A case-crossover study. American Journal of Public Health, 102(12), 2336–2343. https://doi.org/10.2105/AJPH.2012.300762
Vandenbulcke, G., Thomas, I., & Int Panis, L. (2014). Predicting cycling accident risk in Brussels: A spatial case–control approach. Accident Analysis & Prevention, 62, 341–357. https://doi.org/10.1016/j.aap.2013.07.001
Wier, M., Weintraub, J., Humphreys, E. H., Seto, E., & Bhatia, R. (2009). An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accident Analysis & Prevention, 41(1), 137–145. https://doi.org/10.1016/j.aap.2008.10.001
Winters, M., Davidson, G., Kao, D., & Teschke, K. (2011). Motivators and deterrents of bicycling: Comparing influences on decisions to ride. Transportation, 38(1), 153–168. https://doi.org/10.1007/s11116-010-9284-y
Yu, C.-Y. (2014). Environmental supports for walking/biking and traffic safety: Income and ethnicity disparities. Preventive Medicine, 67, 12–16. https://doi.org/10.1016/j.ypmed.2014.06.028
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